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volume 9137 LNCS pages 113-121

On quantum chemistry code adaptation for RSC petastream architecture

Publication typeBook Chapter
Publication date2015-06-19
scimago Q2
SJR0.352
CiteScore2.4
Impact factor
ISSN03029743, 16113349, 18612075, 18612083
Abstract
Molecular simulations with quantum chemistry methods consume a large portion of CPU cycles in modern high-performance computing centers. Evolution of modern processors and HPC architectures necessitates adaptation of software to new hardware generations. The present work concentrates on the optimization of the widely used GAMESS code to Intel Xeon Phi architecture and recently devised RSC PetaStream platform. Since improvement in parallelization is required, the most frequently used Hartree-Fock and DFT methods are explored for additional parallelization options. The Xeon Phi requires vectorization that is important for electron-repulsion integrals (ERI) calculations to achieve good performance.
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Mironov V. et al. On quantum chemistry code adaptation for RSC petastream architecture // Lecture Notes in Computer Science. 2015. Vol. 9137 LNCS. pp. 113-121.
GOST all authors (up to 50) Copy
Mironov V., Khrenova M., Moskovsky A. On quantum chemistry code adaptation for RSC petastream architecture // Lecture Notes in Computer Science. 2015. Vol. 9137 LNCS. pp. 113-121.
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TY - GENERIC
DO - 10.1007/978-3-319-20119-1_9
UR - https://link.springer.com/10.1007/978-3-319-20119-1_9
TI - On quantum chemistry code adaptation for RSC petastream architecture
T2 - Lecture Notes in Computer Science
AU - Mironov, Vladimir
AU - Khrenova, Maria
AU - Moskovsky, Alexander
PY - 2015
DA - 2015/06/19
PB - Springer Nature
SP - 113-121
VL - 9137 LNCS
SN - 0302-9743
SN - 1611-3349
SN - 1861-2075
SN - 1861-2083
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@incollection{2015_Mironov,
author = {Vladimir Mironov and Maria Khrenova and Alexander Moskovsky},
title = {On quantum chemistry code adaptation for RSC petastream architecture},
publisher = {Springer Nature},
year = {2015},
volume = {9137 LNCS},
pages = {113--121},
month = {jun}
}